Due to the availability of technology for non-invasive observation of soft tissues of the human body, significant advances have been made in the field of medicine. For example, a number of machines now make it possible to routinely observe anatomical structures such as the heart, colon, bronchus, and esophagus.
Due to widespread availability of skilled technicians and reduction in cost of the necessary equipment, non-invasive observations can now be employed as a part of routine preventive medicine via periodic examinations. The availability of such non-invasive capabilities both reduces the risk of observation-related injury or complication and reduces discomfort and inconvenience for the observed patient. As a result, patients tend to allow observation to be done more frequently, and medical conditions requiring attention can be detected early. For example, anomalies in an anatomical structure can be identified and diagnosed at an early stage, when treatment is more likely to be successful.
In one popular observation technique called “Computed Tomography Imaging” (“CT Scan”), multiple two-dimensional image slices are taken of a particular section of the patient's body. A physician can then analyze the slices to detect any anomalies within the observed section. Out of the anomalies found, the physician can judge which are anomalies of interest requiring further attention or treatment.
To assure adequate coverage of the section being observed, a large number of slices can be obtained to increase the observation resolution. However, as the number of slices increases, the amount of data presented to the physician becomes overwhelming. Accordingly, various software technologies have been applied with some success to aid the physician in analyzing the data to find anomalies.
Although progress has been made in employing software to assist in detection of anomalies in anatomical structures, there are significant limitations to the current techniques. For example, one problem consistently plaguing such systems is the overabundance of false positives.
Typically, the software approach correctly identifies anomalies of interest (i.e., the software exhibits superior sensitivity). However, the software also tends to incorrectly identify too many anomalies as anomalies of interest (i.e., the software exhibits poor selectivity). An anomaly incorrectly identified as an anomaly of interest is sometimes called a “false positive.”
False positives are troublesome because any identified positives must be considered and evaluated by a human classifier (e.g., the physician). Even if the physician can quickly dismiss an anomaly as a false positive, too many false positives consume an inordinate amount of time and limit the usefulness of the software-based approach.
There thus remains a need for a way to improve the computer-based approaches for identifying anomalies of interest in anatomical structures. For example, selectivity can be improved.